anyone still presuming that a + PCR test is showing a covid case needs to read this v carefully:

even 25 cycles of amplification, 70% of "positives" are not "cases." virus cannot be cultured. it's dead.

by 35: 97% non-clinical.

the US runs at 40, 32X the amplification of 35.
a lot of people still seem to not understand what this means, so let's lay that out for a minute.

PCR tests look for RNA. there is too little in your swab. so they amplify it using a primer based heating and annealing process.

each cycle of this process doubles the material.
the US (and much of the world) is using a 40 Ct (cycle threshold). so, 40 doublings, 1 trillion X amplification.

this is absurdly high.

the way that we know this is by running this test, seeing the Ct to find the RNA, and then using the same sample to try to culture virus.
if you cannot culture the virus, then the virus is "dead." it's inert. if it cannot replicate, it cannot infect you or others. it's just traces of virus, remnants, fragments etc

PCR is not testing for disease, it's testing for a specific RNA pattern

and this is the key pivot
when you crank it up to 25, 70% of the positive results are not really "positives" in any clinical sense.

i hesitate to call it a "false positive" because it's really not. it did find RNA.

but that RNA is not clinically relevant. it cannot make you or anyone else sick
so let's call this a non-clinical positive (NCP).

if 70% of positives are NCP's at 25, imagine what 40 looks like. 35 is 1000X as sensitive.

this study found only 3% live at 35

40 Ct is 32X 35, 32,000X 25

no one can culture live virus past about 34

and we have known this since march. yet no one has adjusted these tests.

this is more very strong data refuting the idea that you can trust a PCR+ as a clinical indicator. that is NOT what it's meant for. at all.

using them to do real time epidemiology is absurd.
the FDA would never do it. the drug companies doing vaccine trials would never do it.

it's because it's nonsense.

and this same test is used for "hospitalizations" and "death with covid" (itself a weirdly over inclusive metric)

PCR testing is not the answer, it's the problem.

it's not how to get control of an epidemic, it's how to completely lose control of your data picture and wind up with gibberish.

and we have done this to ourselves before.

a quick word what this data does and does not mean.

saying "a sample requiring 35 Ct to test + has a 3% real clinical positive rate" does not mean "97% of + tests run at 35 Ct are NCP's"

people seem to get confused on this, so lets explain:
most tests are just amplified and run. they don't test every cycle as these academics do. that would make the test slow and expensive.

so you just run 40 cycles then test.

obviously, a real clinical positive (RCP) that would have been + at 20 is still + at 40.
but when you run the tests each cycle as the academics do, that test would already have dropped out.

so saying that only 3% at 35 are RCP really means that 3% of those samples not PCR + at 34 were PCR and RCP + at 35.

this lets us infer little about overall NCP/RCP rate.
so we cannot say "at 25 Ct, we have a 70 NCP rate." in fact, it's hard to say much of anything. it depends entirely on what the source material coming in looks like.

you cannot even compare like to like.

this is what i mean by "the data is gibberish"
today at 40 Ct, 7% PCR positive rate could be 1% RCP prevalence when that same thing meant 6% RCP prev in april.

if there is lots more trace virus around, more people who have recovered and have fragments left over, etc this test could be finding virus you killed 4 months ago.
so if we consider RCP rate/PCR+ rate, we would expect that number to drop sharply late in an epidemic because there is more dead virus around for PCR to find.

but we have no idea what that ratio is or how it changes.

this spills over in to deaths, reported hospitalization etc.
testing is being made out to be like the high beams on a car. but when it's snowing like hell at night, that is the LAST thing you want.

it is not illuminating our way, it's blinding us.

a bad inaccurate map is much worse than no map at all.

and this is a world class bad map.
we're basing policy that is affecting billions of humans on data that is uninterpretable gibberish.

it's a deranged technocrat's wet dream, but for those of us along for the ride, it's a nightmare.

testing is not the solution, it's the problem.

poison is being peddled as cure.
any technocrat or scientist that does not know this by now is either unfit for their job or has decided that they just don't care and prefer power to morality.

this is, of curse, precisely the kind of person who winds up running a gov't agency.

the head of the NIH is not the best scientist, it's the best politician.

all this wild and reckless government policy has never been about the science.

it's politics and panic.

you can read the whole paper here:…

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More from @boriquagato

23 Nov
this is not a study.

it's based on a model that estimated that X% of spread is from asymptomatic and then, surprise! got that as output.

this is literally saying "my model says that if i multiple X by 3, it triples!"

how is this passing for science to inform policy?
the CNN article he cites cites this "study" which is not a study at all.

it's a complex mathematical model that has long since fallen on its face.

this is, at best, extremely misleading.…
it's a percolation model build on bad premises.

they assume that PCR+ is a "case" and that spread must be "asymptomatic" when this is just the recipe for a casedemic from over-testing.

they then assume their premise and claim that a remainder must be asympt spread. Image
Read 5 tweets
23 Nov
CDC claims that masks stopped the spread of covid in kansas by comparing masked and non-masked counties.

counterpoint: this was a cherry pick in terms of date and seasonality.

they ended the "study" aug 23.

then, covid season hit and the masks look to have made no difference. Image
how is it that every time someone has a "masks work" study, as soon as you look at how they did it, the methodology falls apart?

"team mask" has been relentless in pushing tragically, offensively bad science and top officials like gottlieb parrot it.

did he even read it?
because it's clearly a hilariously bad methodology and we've now had more data to check the hypothesis.

this study was released with the data that disproves it already available.

let's repeat that:

this study was released with the data that disproves it already available.
Read 6 tweets
23 Nov
@PiranhaCapital @real_MikeBarnes @GCA_Worldwide there is lots or hard evidence for biome specific immunity.

SARS-1 never spread to the US. neither did MERS.

this study of pre-covid 19 blood samples shows 10-15X the prevalence of sars-2 antibodies in africa vs US

@PiranhaCapital @real_MikeBarnes @GCA_Worldwide regional virus exposure is not that uncommon and similarity to sars-2 does not necessarily imply contagion like sars-2.

you could spend years generation localized cross resistance from endemic or source based viruses with low R.

then you get a breakout variant like sars-2
@PiranhaCapital @real_MikeBarnes @GCA_Worldwide the idea that lockdowns work looks pretty fraught. every standing set of pandemic guidelines from 2020 said they do not and no contrary evidence has emerged since.

asia has varies responses, all got same results.

no way this was just NPI.

Read 4 tweets
23 Nov
there seems to be a lot of grandstanding by the lockdown governors (who are almost invariably democrats as this issue has become distressingly partisan) but when one looks at red state vs blue state outcomes, it raises some serious questions.

(graph from david steinmier, PhD) Image
the blue states are faring noticeably less well than the red.

this has been true all along.

interestingly, red states are noticeably fatter which is a significant cov risk enhancer (and links to others like diabetes)

so, ceteris paribus, you'd expect more red state deaths. Image
the rejoinder to this has always been "but density!" but this has been a surprisingly poor predictor of death rates globally.

most of the data is highly uncorrelated with a few outliers driving outcomes. county level is a little better, but not great. globally, it's worse. Image
Read 11 tweets
22 Nov
how to lie with correlations: harley davidson limited edition.

there has been a lot of aggressive talk about how sturgis was a massive super spreader event

charts like this make it tempting to agree.

but be very careful about doing so: there's a big issue with this graph Image
it's from 5000 miles away and across an ocean.

as one can rapidly see "cases" measured here are in eastern europe, not the american midwest.

hard to imagine the harley folk having caused this no matter how hard they partied. Image
interestingly enough, south dakota and czechia look incredibly similar in terms of disease curve (deaths per million population) despite having totally different responses.

(the CZE data is real day of death, so it lags. the last 10-14 days are likely incomplete) Image
Read 8 tweets
21 Nov
several months back, i posited that the low covid deaths in pac rim could not possibly be from lockdowns or masks.

the differential was simply too large, the policies across the region too varied, and the results to internally similar.

it has to be pre-existing resistance.
i'd like to now revisit this hypothesis as quite a bit of new evidence has emerged and i think it has been increasingly supportive of this idea.

the gaps are simply too large and too geographically consistent.

i am becoming convinced that this is the only plausible explanation.
i'm going to use deaths as a metric because testing in asia has been very low in many places (1-10% of western per capita levels) and trying to adjust for that makes gibberish out of the data.
Read 29 tweets

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